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MIS 304: Using and Managing Information Systems Lab Session 5: Business Intelligence Analysis Decision Tree The goal of this lab is to help you get started with RapidMiner for Business Intelligence

1 MIS 304: Using and Managing Information Systems Lab Session 5: Business Intelligence Analysis Decision Tree The goal of this lab is to help you get started with RapidMiner for Business Intelligence (BI) Analysis. RapidMiner is a very popular predictive analytics platform which uses a graphical user interface. It brings the power of predictive analytics to the business user, with NO programming required. RapidMiner supports many different BI techniques, but we will focus only on decision trees here (Read: We will first install the RapidMiner Starter Edition. Then we will analyze a customer churn data with RapidMiner and build a decision tree model. 1. Install RapidMiner (1) Download RapidMiner Starter Edition (free): 1 P age

2 (2) Double-click on the file that was downloaded above. You may have a question asking you if you really want to run the file. Click OK. Then click Next. (3) Click I Agree. (4) Change where you install RapidMiner if you want. Click Install to continue. 2 P age

3 (5) Click Next. Then Finish. (6) Test if you can open RapidMiner. (7) Click on "Continue using RapidMiner Studio Basic" (You can create your account later if you want to). 3 P age

4 (8) The new process page should open. Below is a brief description of important areas in RapidMiner. Repository Tab Process Panel Parameters Area Operators Tab Problems / Log Area Process Help Area 4 P age

5 Process Panel is used to design any process you want to take on your data, for examples, data loading, classification and predication. Process is comprised of a set of connected operators, which can be understood as the building blocks of your BI analysis process. 2. Download customer-churn-data.xlsx from Blackboard. Take a look at the data file. Information about customers gender, age, payment methods and last transactions are included in the dataset. We are trying to know if they are loyal customers based on these information. 3. Data analysis and decision tree building. (1) Import dataset into Rapidminer. We need to choose the operator that imports from Excel files. There are several ways to find the operator we are looking for: (i) Know which group the operator belongs to, and just expand the list until we find it. (ii) Start typing what we think the operator might be called into the filter field under the Operators Tab, so that the software will start showing suggestion as you type. The operator we need here is called Read Excel. 5 P age

We will use the Import Configuration Wizard button to specify our parameters.

6 To import the data correctly we need to specify certain parameters, such as where the Excel file exists, which sheet(s) to extract from, columns, cell range etc. We will use the Import Configuration Wizard button to specify our parameters. Click First, locate where the Excel file is. Once the required Excel file customer-churn-data.xlsx is located, click Next. 6 P age

The cells should be highlighted as shown in below. Then click Next.

7 Here we can choose the appropriate sheet and cell range. Our data resides in the customer data worksheet, and columns A through E are needed. The cells should be highlighted as shown in below. Then click Next. Then, make sure each attribute is annotated. Under Annotation choose Name for the column titles row as shown in below. Click Next. 7 P age

To do so, we go to Churn, to the cell that says attribute, which is above

8 Now we choose which attribute (column) we are trying to forecast based on the Data we have. In our case that is Churn. To do so, we go to Churn, to the cell that says attribute, which is above our actual data, and from the drop-down menu change it from attribute to label. Then click Finish. 8 P age

On each operator if you have a yellow triangle with an exclamation mark, that usually means something is wrong

9 (2) Now that the Read Excel preferences are set up, we need to select the Decision Tree Operator to actually produce the decision tree we desire. Change parameter maximal depth to 5 for the Decision Tree Operator. On each operator if you have a yellow triangle with an exclamation mark, that usually means something is wrong with your setup and needs to be rectified before proceeding further, otherwise your model will probably not run. For example: 9 P age

10 Here we need to connect Read Excel and Decision Tree on out and tra (tra = training). Next, connect Decision Tree and Process Panel on their mod (mod = model) and res (res = results) Then click the Run button. 10 P age

5 are loyal customers. If you are the manager of this company, what would you do in the future?

11 You should get your results the decision tree. We can find that men are more loyal customers than women for this company. Women whose ages are greater than 89.5 are loyal customers. If you are the manager of this company, what would you do in the future? Deliverables Take a screen shot of your decision tree and paste it on a word file yournetid_l5.docx, submit in TurnItIn. ** Copyright: Originally created by Wenli Zhang for MIS P age

Checking whether the protocol was followed: gender and age 51

Checking whether the protocol was followed: gender and age 51 Session 4: Checking whether the protocol was followed: gender and age In the data cleaning workbook there are two worksheets which form the